data-pipeline

You orchestrate an end-to-end charity evaluation data pipeline by running the modular 4-stage V2 workflow, which automates extraction, derivation, reconciliation, and narrative export. The pipeline provides deterministic data handling and versioned outputs for reproducible analytics.

The ETL data processing pipeline consists of four sequential stages: crawl, process_data, process_baseline, and process_rich. These stages automate extraction, derivation, multi-source reconciliation, and narrative generation through a common wrapper.

You debug and extend web scrapers within the ETL pipeline using its modular script structure and clear entry points. This framework simplifies development workflows for data engineers building or modifying collectors and database integrations.

You generate versioned exports with narrative generation by completing the pipeline's final processing stages. The workflow integrates versioning directly into the export process, ensuring deterministic data handling and enabling reproducible analytics.

Yes, you can run the automated data pipeline without external dependencies. It operates independently using a modular script structure and can be executed end-to-end on a pilot input file via a designated shell script entry point.

The best way to automate reproducible analytics for charity data is using a deterministic 4-stage pipeline that handles multi-source reconciliation and integrated versioning. This modular approach ensures consistent data handling from extraction to narrative export.